Two-stage object detectors split the detection process into two steps. The first step is a convolutional region network, a.k.a, region based convolutional neural networks (R-CNN). It extracts feature maps from the input image and provides a certain number of regions called regions of interest (ROI). ROIs are then classified in the second step via a dense neural network to provide the final bounding boxes that locates individuals. The R-CNN is the foundation of most two-stage object detectors [1]}. Faster R-CNN, proposed in [2]}, managed to reach a mean average precision (mAP) of 69% on the PASCAL visual object classes (VOC) 2007 dataset in [3]}.
